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Incremental segmentation of lidar point clouds with an octree-structured voxel space / M. Wang in Photogrammetric record, vol 26 n° 133 (March - May 2011)
[article]
Titre : Incremental segmentation of lidar point clouds with an octree-structured voxel space Type de document : Article/Communication Auteurs : M. Wang, Auteur ; Yi-Hsing Tseng, Auteur Année de publication : 2011 Article en page(s) : pp 32 - 57 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] coplanarité
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] octree
[Termes IGN] reconstruction d'objet
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] voxelRésumé : (Auteur) Lidar (light detection and ranging) data implicitly contains abundant three-dimensional spatial information. The segmentation of lidar point clouds is the key procedure for transforming implicit spatial information into explicit spatial information. Common criteria used for point cloud segmentation are proximity and coherence of point distribution. An effective segmentation algorithm may apply various steps or combinations of criteria depending on the application. This paper proposes a four-step segmentation method for lidar point clouds to deliver incremental segmentation results. Segmentation results of each step can provide the fundamental data for the next step. In the first step, the input point cloud is organised into an octree-structured voxel space, in which the point neighbourhood is established. In the second step, connected voxels which are not empty are grouped to obtain grouped points based on proximity. The third step is a coplanar point segmentation based on both coherence and proximity, which was performed on each point group obtained in the second step. Finally, neighbouring coplanar point groups are merged into “co-surface” point groups based on the criteria of plane connection and intersection. This scheme enables an incremental retrieval and analysis of a large lidar data-set. Experimental results demonstrate the effectiveness of the segmentation algorithm in handling both airborne and terrestrial lidar data. It is anticipated that the incremental segmentation results will be useful for object modelling using lidar data. Numéro de notice : A2011-077 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/j.1477-9730.2011.00624.x Date de publication en ligne : 16/03/2011 En ligne : https://doi.org/10.1111/j.1477-9730.2011.00624.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30858
in Photogrammetric record > vol 26 n° 133 (March - May 2011) . - pp 32 - 57[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2011011 RAB Revue Centre de documentation En réserve L003 Disponible Automatic segmentation of Lidar data into coplanar point clusters using an octree-based split-and-merge algorithm / M. Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 4 (April 2010)
[article]
Titre : Automatic segmentation of Lidar data into coplanar point clusters using an octree-based split-and-merge algorithm Type de document : Article/Communication Auteurs : M. Wang, Auteur ; Yi-Hsing Tseng, Auteur Année de publication : 2010 Article en page(s) : pp 407 - 420 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fusion d'images
[Termes IGN] modélisation 3D
[Termes IGN] octree
[Termes IGN] segmentation
[Termes IGN] segmentation en plan
[Termes IGN] semis de pointsRésumé : (Auteur) Lidar (light detection and ranging) point cloud data contain abundant three-dimensional (3D) information. Dense distribution of scanned points on object surfaces prominently implies surface features. Particularly, plane features commonly appear in a typical lidar dataset of artificial structures. To explore implicitly contained spatial information, this study developed an automatic scheme to segment a lidar point cloud dataset into coplanar point clusters. The central mechanism of the proposed method is a split-and-merge segmentation based on an octree structure. Plane fitting serves as an engine in the mechanism that evaluates how well a group of points fits to a plane. Segmented coplanar points and derived parameters of their best-fit plane are obtained through the process. This paper also provides algorithms to derive various geometric properties of segmented coplanar points, including inherent properties of a plane, intersections of planes, and properties of point distribution on a plane. Several successful cases of handling airborne and terrestrial lidar data as well as a combination of the two are demonstrated. This method should improve the efficiency of object modelling using lidar data. Copyright ASPRS Numéro de notice : A2010-121 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.76.4.407 En ligne : https://doi.org/10.14358/PERS.76.4.407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30317
in Photogrammetric Engineering & Remote Sensing, PERS > vol 76 n° 4 (April 2010) . - pp 407 - 420[article]